AIR POLLUTION – MONITORING MODELLING AND HEALTH

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256 Air Pollution – Monitoring, Modelling and Health Finally, the importance chart for the RBF network (Fig. 8) shows that, once again, GDP growth rate and GDP are the most important predictors of sulphur and nitrogen oxides emissions. Fig. 8. RBF Independent variable importance chart. 4. Conclusions The multi-layer perceptron and radial-basis function neural network models, that were trained to predict air quality indicators, using life quality and welfare indicators, appear to perform reasonably well. Unlike traditional statistical methods, the neural network models provide dynamic output as further data is fed to it, while they do not require performing and analyzing sophisticated statistical methods (Narasinga Rao et al., 2010). Results showed that GDP growth rate and GDP influenced mainly air quality predictions, while life expectancy, infant mortality and healthy life years followed distantly. One possible way to ameliorate performance of the network would be to create multiple networks. One network would predict the country result, perhaps simply whether the country increased emissions or not, and then separate networks would predict emissions conditional on whether the country increased emissions. We could then combine the network results to likely obtain better predictions. Note also that neural network is open ended; as more data is given to the model, the prediction would become more reliable. Overall, we find that predictors that include economic indices may be employed by investigators to represent dimensions of air quality that include, as well as go beyond, these simple indices.
Developing Neural Networks to Investigate Relationships Between Air Quality and Quality of Life Indicators 257 5. References Bishop, C. (1995). Neural Networks for Pattern Recognition, 3 rd ed. Oxford University Press, Oxford. Bors, A., Pitas, I. (2001). Radial Basis function networks In: Howlett, R., Jain, L (eds.). Recent Developments in Theory and Applications in Robust RBF Networks, 125-153 Heidelberg, NY, Physica-Verlag. Bresnahan, B., Mark, D., Shelby, G. (1997). Averting behavior and urban air pollution. Land Economics 73, 340–357. Callan, R. (1999). The Essence of Neural Networks. Prentice Hall, UK . Dendek, C., Mańdziuk, J. (2008). Improving Performance of a Binary Classifier by Training Set Selection. Warsaw University of Technology, Faculty of Mathematics and Information Science, Warsaw, Poland. Dogra, Shaillay, K. (2010). Autoscaling. QSARWorld - A Strand Life Sciences Web Resource. http://www.qsarworld.com/qsar-statistics-autoscaling.php Eurostat. (2010). http://epp.eurostat.ec.europa.eu. Fine, T. (1999). Feedforward Neural Network Methodology, 3 rd ed. Springer-Verlag, New York. Flynn P., Berry D., Heintz T. (2002). Sustainability & Quality of life indicators: Towards the Integration of Economic, Social and Environmental Measures. The Journal of Social Health 1(4), 19-39. Haykin, S. (1998). Neural Networks: A Comprehensive Foundation, 2 nd ed. Prentice Hall, UK. Hirschberg, J., Esfandiar, M., Slottje, D. (1991). Cluster analysis for measuring welfare and quality of life across countries. Journal of Econometrics 50, 131–150. Hirschberg, J., Maasoumi, E., Slottje, D. (1998). A cluster analysis the quality of life in the United States over time. Department of Economics research paper #596, University of Melbourne, Parkville, Australia. IBM. (2010). SPSS Neural Networks 19. SPSS Inc, USA. Iliadis, L. (2007). Intelligent Information Systems and Applications in Risk Management. Stamoulis editions, Thessaloniki, Greece. Kecman, V. (2001). Learning and Soft Computing. MIT Press, London. Koller, M., Lorenz, W. (2003). Survival of the quality of life concept. British Journal of Surgery 90(10), 1175-1177. Maasoumi, E. (1998). Multidimensional approaches to welfare. In: Silber, L. (ed.). Income Inequality Measurement: From Theory to Practice. Kluwer, New York. Pandey, M., Nathwani, J. (2004). Life quality index for the estimation of social willingness to pay for safety. Structural Safety 26(2), 181-199. Picton, P. (2000). Neural Networks, 2 nd ed. Palgrave, New York. Narasinga Rao, M., Sridhar, G., Madhu, K., Appa Rao, A. (2010). A clinical decision support system using multi-layer perceptron neural network to predict quality of life in diabetes. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 4, 57–59. Ripley, B. (1996). Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge. Slottje, D., Scully, G., Hirschberg, J., Hayes, K. (1991). Measuring the Quality of Life Across Countries: A Multidimensional Analysis. Westview Press, Boulder, CO.
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AIR POLLUTION - MONITORING, MODELLI
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Contents Preface IX Chapter 1 Urban
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Preface This book provides a compre
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